Charlie Holtz’s Post

Melty, the new open-source AI code editor designed for 10x engineers. Melty is the first AI code editor that understands everything you do—from the terminal to GitHub—and actively collaborates with you to write production-ready code. Here’s how it stands out: • It helps you understand your code better, not worse. • Works like a true pair programmer, watching every change you make. • Adapts and learns your codebase. • Seamlessly integrates with your compiler, terminal, debugger, and tools like Linear & GitHub. Ready to boost your productivity? Learn more: https://melty.sh

Let’s get real: frameworks are the Swiss Army knives of the tech world. From whipping up a Next.js web app to deploying a PyTorch ML model, these tools are indispensable. They’re like magic wands that transform months of coding into days. But here's the kicker: if all you know is how to wave the wand, you’re missing the magic behind it. Blog: https://lnkd.in/dyEA2QTv

Big tech is aggressively promoting AI code assistants. All the tools that I have used fall short in significant ways. In addition to their shortcomings, they also lock you into their ecosystem and are not transparent about how they use your data or how the LLM works. An open-source project called Aider (https://aider.chat/) is miles ahead of the commercial alternatives. It has an interface and features that match what it is like to pair a program with a colleague. The key differences are that Aider can: - Map your whole repository. - Make complex changes across multiple source files. - Run commands and use the output in subsequent steps. - Allows you to use many LLM models, both proprietary and open-source. - Updates the context it uses for prediction while updating it. - Store the entire chat history of each repo you work on as a markdown file for later reference. I was fascinated to learn that Aider wrote 82% of the last release itself! The project maintains an LLM leaderboard: https://lnkd.in/dJWJxN2S

Meet BigCodeBench by BigCode: The New Gold Standard for Evaluating Large Language Models on Real-World Coding Tasks BigCode, a leading entity in developing large language models

Meet BigCodeBench by BigCode: The New Gold Standard for Evaluating Large Language Models on Real-World Coding Tasks BigCode, a leading entity in developing large language models

I'm joining the first ever YCombinator fall batch for the next 3 months with my cofounder Nathan Ang. We are building the ultimate open source AI code editor of the future, PearAI. Think a better Copilot, or open source Cursor. We launched 3 weeks ago and already have over 300 daily active users. We're a fork of VSCode (and Continue.dev!), so you can maintain familiarity and functionalities, and not worry about switching effort costs. People who have been using it so far expressed that PearAI has up to 5x’d their work and development speed by having AI integrated features such as the ability to talk to your codebase directly. Students also said this accelerated their learning tremendously, with features like /leetcode and /sensei for beginner friendly tasks. Try it out and let me know how you like it! We have a built-in free trial, download for free at https://lnkd.in/e5PeBamv :) Here's a quick demo where I make a documentation page for our landing site without writing a single line of code!

Spot on analysis! Firing an entire dev team and relying solely on AI coding tools isn't just a misunderstanding of technology—it's a fundamental misstep in risk management and long-term strategy. LLMs, like any transformative tool, amplify human creativity and productivity but can’t replicate the nuanced decision-making and deep contextual understanding that experienced developers bring to the table. The best outcomes come from synergistic use: humans leveraging AI to solve complex problems faster, not replacing them outright. At #EndavaGames, we’re constantly evaluating how AI can enhance our workflows, especially in debugging and optimization, but always with the understanding that people remain the core of our innovation. It's exciting to embrace the future, but the hype should never overshadow the fundamentals of building great teams and sustainable systems.

📢 𝗜𝘀𝘀𝘂𝗲 𝟰𝟯𝟮 𝗶𝘀 𝗢𝘂𝘁! In today's edition, you can find: 📰 𝗡𝗲𝘄𝘀: - Nvidia ousts Intel from Dow Jones Index after 25-year run - Perplexity CEO offers to replace striking NYT staff with AI - ChatGPT Search is not OpenAI's 'Google killer' yet ... and more! 💡📚 𝗔𝗿𝘁𝗶𝗰𝗹𝗲𝘀: - OOPS in JS - Ultimate (by Subham M.) - 🔥12 Best AI Coding Assistant Tools for Devs🧑💻 (by Kiran Naragund) - Re-discovering the Joy of Building (by Arpit Nath) - Mastering Clean Code with SOLID, DRY, KISS, and YAGNI Principles (by Himanshu Singour) ☠️ 𝗣𝗼𝘀𝘁𝗺𝗼𝗿𝘁𝗲𝗺: PagerDuty's third-party SMS and voice service failed due to AWS outage. 👨💻 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝗶𝗲𝘀: From Binance Spot API docs to high-performance HTML/CSS renderer. (by Binance Paradigm Dioxus Labs) 🔗 𝗖𝗵𝗲𝗰𝗸 𝗶𝘁 𝗼𝘂𝘁: https://lnkd.in/eVhwm-rV 💌 Subscribe to get fresh news every day: https://0xcafe.news

Just read about a founder who fired their entire dev team and bet everything on AI coding tools. r/LinkedInLunatics much?? Here’s the thing: current LLMs are great force multipliers, but they’re not outright replacements - especially for programming. Used properly, they boost productivity like any game-changing tech. But expecting them to replicate the judgment, context, and strategic thinking of devs? Sorry, missing the point. The real magic lies in humans working alongside LLMs. (We’re actually exploring how to better integrate this approach into our debugging platform - would love to hear your thoughts and suggestions!) Also, firing your whole dev team reveals a deeper misunderstanding of business risk, and shows how it's way too easy to get swept up in the hype around this new tech.

The open-source AI community has a new champion in code generation: Qwen 2.5 Coder. This latest model, particularly the 32B version, demonstrates impressive performance on popular coding benchmarks, rivaling even OpenAI's GPT-4o. Qwen 2.5 Coder boasts several key advantages: ✅ Multi-language proficiency: It excels in over 40 programming languages, including Haskell and Racket. ✅ Code repair capabilities: It effectively identifies and fixes errors in code across various languages. ✅ Preference alignment: It demonstrates strong alignment with human preferences for code generation. Developers can leverage Qwen 2.5 Coder's power through its various model sizes (7B, 14B, 32B) designed for different resource constraints. This LLM in our AI chatbot allows a seamless application (via the Continue plugin) within development environments such as Visual Studio Code or JetBrains IDEs.